Paper Info
Title
A Novel Approach for Detecting Browser-Based Silent Miner
Abstract
Transactions in the cryptocurrency market has been extremely hot in recent years, with the price of cryptocurrency climbing all the way. Hackers have turned their attentions to cryptocurrencies, and have used various means to acquire cryptocurrencies illegally, which caused huge losses to the victims. Some browsers block malicious mining activities from the network protocol level, but they do not have the ability to detect mining samples themselves, and it is difficult to make effective detection of homogenous mining samples of the network layer. To solve these problems, based on the attack pattern of browser mining, the browser-based silent mining features are analyzed, and a method to detect browser silent mining behavior is proposed. This method drives known malicious mining samples, extracts heap snapshots and stack code features of a dynamically running browser, and performs automated detection based on recurrent neural network. By modifying the kernel code of Chrome, a browser-based silent miner detection prototype system BMDetector was designed and implemented. With 1159 samples detected and analyzed, experimental results show that the recognition rate of the original mining sample is 98%, and 92% for the encrypted and confused, which is an effective and feasible method.
Year
DOI
Venue
2018
10.1109/DSC.2018.00079
2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)
Keywords
Field
DocType
Cryptocurrency,Miner,Browser,Dynamic detection,RNN
Kernel (linear algebra),Data mining,Computer science,Network layer,Recurrent neural network,Encryption,Feature extraction,Heap (data structure),Cryptocurrency,Communications protocol
Conference
ISBN
Citations 
PageRank 
978-1-5386-4211-5
1
0.40
References 
Authors
1
5
Name
Order
Citations
PageRank
Jingqiang Liu140.77
Zihao Zhao210.40
Xiang Cui311520.63
Zhi Wang47614.27
Qixu Liu510415.78